Machine learning models for predicting vegetation conditions in Mahanadi River basin
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s10661-023-12006-x.pdf
Reference39 articles.
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2. Abera, T. A., Heiskanen, J., Pellikka, P., & Maeda, E. E. (2018). Rainfall–vegetation interaction regulates temperature anomalies during extreme dry events in the Horn of Africa. Global and Planetary Change, 167, 35–45. https://doi.org/10.1016/j.gloplacha.2018.05.002
3. Ahmad, R., Yang, B., Ettlin, G., Berger, A., & Rodríguez-Bocca, P. (2023). A machine-learning based ConvLSTM architecture for NDVI forecasting. International Transactions in Operational Research, 30(4), 2025–2048. https://doi.org/10.1111/itor.12887
4. Chang, C. T., Lin, T. C., Wang, S. F., & Vadeboncoeur, M. A. (2011). Assessing growing season beginning and end dates and their relation to climate in Taiwan using satellite data. International Journal of Remote Sensing, 32(18), 5035–5058. https://doi.org/10.1080/01431161.2010.494635
5. Deng, Y., Wang, S., Bai, X., Tian, Y., Wu, L., Xiao, J., Chen, F., & Qian, Q. (2018). Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific Reports, 8(1), 1–12. https://doi.org/10.1038/s41598-017-19088-x
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